OpenRaiser/Intern-Atlas huggingface dataset:
Gazelle: A Low Latency Framework for Secure Neural Network Inference
Venue: arXiv cs.CR 2018
Year: 2018

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time.
rlsm: R package for least squares Monte Carlo
Venue: arXiv cs.MS 2018
Year: 2018

This short paper briefly describes the implementation of the least squares Monte Carlo method in the rlsm package. This package provides users with an easy manner to experiment with the large amount of R regression tools on any regression basis and reward functions. This package also computes lower and upper bounds for the true value function via duality methods.
Exploiting Diversity in Molecular Timing Channels via Order Statistics
Venue: arXiv cs.IT 2018
Year: 2018

We study diversity in one-shot communication over molecular timing channels. We consider a channel model where the transmitter simultaneously releases a large number of information particles, while the information is encoded in the time of release. The receiver decodes the information based on the random time of arrival of the information particles. The random propagation is characterized by the general class of right-sided unimodal densities. We characterize the asymptotic exponential decrease rate of the probability of error as a function of the number of released particles, and denote this quantity as the system diversity gain. Four types of detectors are considered: the maximum-likelihood (ML) detector, a linear detector, a detector that is based on the first arrival (FA) among all the transmitted particles, and a detector based on the last arrival (LA). When the density characterizing the random propagation is supported over a large interval, we show that the simple FA detector achieves a diversity gain very close to that of the ML detector. On the other hand, when the density characterizing the random propagation is supported over a small interval, we show that the simple LA detector achieves a diversity gain very close to that of the ML detector.
The Data Market: Policies for Decentralised Visual Localisation
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a mercantile framework for the decentralised sharing of navigation expertise amongst a fleet of robots which perform regular missions into a common but variable environment. We build on our earlier work and allow individual agents to intermittently initiate trades based on a real-time assessment of the nature of their missions or demand for localisation capability, and to choose trading partners with discrimination based on an internally evolving set of beliefs in the expected value of trading with each other member of the team. To this end, we suggest some obligatory properties that a formalisation of the distributed versioning of experience maps should exhibit, to ensure the eventual convergence in the state of each agent's map under a sequence of pairwise exchanges, as well as the uninterrupted integrity of the representation under versioning operations. To mitigate limitations in hardware and network resources, the "data market" is catalogued by distinct sections of the world, which the agents treat as "products" for appraisal and purchase. To this end, we demonstrate and evaluate our system using the publicly available Oxford RobotCar Dataset, the hand-labelled data market catalogue (approaching 446km of fully indexed sections-of-interest) for which we plan to release alongside the existing raw stereo imagery. We show that, by refining market policies over time, agents achieve improved localisation in a directed and accelerated manner.
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Venue: arXiv cs.DC 2018
Year: 2018

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
Venue: arXiv cs.LG 2018
Year: 2018

In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably class imbalance and covariate shift. In this work, we aim to shed light on this topic in order to increase the overall attention to this issue in the field of machine learning. We propose a scalable novel framework for reducing multiple biases in high-dimensional data sets in order to train more reliable predictors. We apply our methodology to the detection of irregular power usage from real, noisy industrial data. In emerging markets, irregular power usage, and electricity theft in particular, may range up to 40% of the total electricity distributed. Biased data sets are of particular issue in this domain. We show that reducing these biases increases the accuracy of the trained predictors. Our models have the potential to generate significant economic value in a real world application, as they are being deployed in a commercial software for the detection of irregular power usage.
Compact Real-time avoidance on a Humanoid Robot for Human-robot Interaction
Venue: arXiv cs.RO 2018
Year: 2018

With robots leaving factories and entering less controlled domains, possibly sharing the space with humans, safety is paramount and multimodal awareness of the body surface and the surrounding environment is fundamental. Taking inspiration from peripersonal space representations in humans, we present a framework on a humanoid robot that dynamically maintains such a protective safety zone, composed of the following main components: (i) a human 2D keypoints estimation pipeline employing a deep learning based algorithm, extended here into 3D using disparity; (ii) a distributed peripersonal space representation around the robot's body parts; (iii) a reaching controller that incorporates all obstacles entering the robot's safety zone on the fly into the task. Pilot experiments demonstrate that an effective safety margin between the robot's and the human's body parts is kept. The proposed solution is flexible and versatile since the safety zone around individual robot and human body parts can be selectively modulated---here we demonstrate stronger avoidance of the human head compared to rest of the body. Our system works in real time and is self-contained, with no external sensory equipment and use of onboard cameras only.
In-network Neural Networks
Venue: arXiv cs.DC 2018
Year: 2018

We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers. Our system shows that these devices can run simple neural network models, whose input is encoded in the network packets' header, at packet processing speeds (billions of packets per second). Furthermore, our experience highlights that switching chips could support even more complex models, provided that some minor and cheap modifications to the chip's design are applied. We believe N2Net provides an interesting building block for future end-to-end networked systems.
Towards a computational model of social norms
Venue: arXiv cs.CY 2018
Year: 2018

We describe a computational model of social norms based on identifying values that a certain culture finds desirable such as dignity, generosity and politeness. The model quantifies these values in the form of Culture-Sanctioned Social Metrics (CSSMs) and treats social norms as the requirement to maximize these metrics from the perspective of the self, peers and public. This model can be used to create realistic social simulations, to explain or predict human behavior in specific scenarios, or as a component of robots or agents that need to interact with humans in specific social-cultural settings. We validate the model by using it to represent a complex deception scenario and showing that it can yield non-trivial insights such as the explanation of apparently irrational human behavior.
Interactive in-base street model edit: how common GIS software and a database can serve as a custom Graphical User Interface
Venue: arXiv cs.DB 2018
Year: 2018

Our modern world produces an increasing quantity of data, and especially geospatial data, with advance of sensing technologies, and growing complexity and organisation of vector data. Tools are needed to efficiently create and edit those vector geospatial data. Procedural generation has been a tool of choice to generate strongly organised data, yet it may be hard to control. Because those data may be involved to take consequence-full real life decisions, user interactions are required to check data and edit it. The classical process to do so would be to build an adhoc Graphical User Interface (GUI) tool adapted for the model and method being used. This task is difficult, takes a large amount of resources, and is very specific to one model, making it hard to share and re-use. Besides, many common generic GUI already exists to edit vector data, each having its specialities. We propose a change of paradigm; instead of building a specific tool for one task, we use common GIS software as GUIs, and deport the specific interactions from the software to within the database. In this paradigm, GIS software simply modify geometry and attributes of database layers, and those changes are used by the database to perform automated task. This new paradigm has many advantages. The first one is genericity. With in-base interaction, any GIS software can be used to perform edition, whatever the software is a Desktop sofware or a web application. The second is concurrency and coherency. Because interaction is in-base, use of database features allows seamless multi-user work, and can guarantee that the data is in a coherent state. Last we propose tools to facilitate multi-user edits, both during the edit phase (each user knows what areas are edited by other users), and before and after edit (planning of edit, analyse of edited areas).
Performance Analysis of Joint Pairing and Mode Selection in D2D Communications with FD Radios
Venue: arXiv cs.IT 2018
Year: 2018

In cellular-D2D networks, users can select the communication mode either direct and form D2D links or indirect and communicate with BS. In former case, users should perform pairing selection and choose their pairs. The main focus in this paper is proposing an analytical framework by using tools from stochastic geometry to address these two issues, i.e. i) mode selection for the user devices to be established in either cellular or D2D mode, which is done based on received power from BS influenced by a bias factor, and ii) investigation of choosing nth-nearest neighbor as the serving node for the receiver of interest, by considering full-duplex (FD) radios as well as half- duplex (HD) in the D2D links. The analytic and simulation results demonstrate that even though the bias factor determines the throughput of each mode, it does not have any influence on the system sum throughput. Furthermore, we demonstrate that despite of suffering from self-interference, FD-D2D results in higher system sum throughput as well as higher coverage probability in comparison to its counterpart, namely purely HD- D2D network.
On Spectral Graph Embedding: A Non-Backtracking Perspective and Graph Approximation
Venue: arXiv cs.SI 2018
Year: 2018

Graph embedding has been proven to be efficient and effective in facilitating graph analysis. In this paper, we present a novel spectral framework called NOn-Backtracking Embedding (NOBE), which offers a new perspective that organizes graph data at a deep level by tracking the flow traversing on the edges with backtracking prohibited. Further, by analyzing the non-backtracking process, a technique called graph approximation is devised, which provides a channel to transform the spectral decomposition on an edge-to-edge matrix to that on a node-to-node matrix. Theoretical guarantees are provided by bounding the difference between the corresponding eigenvalues of the original graph and its graph approximation. Extensive experiments conducted on various real-world networks demonstrate the efficacy of our methods on both macroscopic and microscopic levels, including clustering and structural hole spanner detection.
A Pipeline for Post-Crisis Twitter Data Acquisition
Venue: arXiv cs.IR 2018
Year: 2018

Due to instant availability of data on social media platforms like Twitter, and advances in machine learning and data management technology, real-time crisis informatics has emerged as a prolific research area in the last decade. Although several benchmarks are now available, especially on portals like CrisisLex, an important, practical problem that has not been addressed thus far is the rapid acquisition and benchmarking of data from free, publicly available streams like the Twitter API. In this paper, we present ongoing work on a pipeline for facilitating immediate post-crisis data collection, curation and relevance filtering from the Twitter API. The pipeline is minimally supervised, alleviating the need for feature engineering by including a judicious mix of data preprocessing and fast text embeddings, along with an active learning framework. We illustrate the utility of the pipeline by describing a recent case study wherein it was used to collect and analyze millions of tweets in the immediate aftermath of the Las Vegas shootings.
Rate-Optimal Streaming Codes for Channels with Burst and Isolated Erasures
Venue: arXiv cs.IT 2018
Year: 2018

Recovery of data packets from packet erasures in a timely manner is critical for many streaming applications. An early paper by Martinian and Sundberg introduced a framework for streaming codes and designed rate-optimal codes that permit delay-constrained recovery from an erasure burst of length up to $B$. A recent work by Badr et al. extended this result and introduced a sliding-window channel model $\mathcal{C}(N,B,W)$. Under this model, in a sliding-window of width $W$, one of the following erasure patterns are possible (i) a burst of length at most $B$ or (ii) at most $N$ (possibly non-contiguous) arbitrary erasures. Badr et al. obtained a rate upper bound for streaming codes that can recover with a time delay $T$, from any erasure patterns permissible under the $\mathcal{C}(N,B,W)$ model. However, constructions matching the bound were absent, except for a few parameter sets. In this paper, we present an explicit family of codes that achieves the rate upper bound for all feasible parameters $N$, $B$, $W$ and $T$.
The Utility Cost of Robust Privacy Guarantees
Venue: arXiv cs.IT 2018
Year: 2018

Consider a data publishing setting for a data set with public and private features. The objective of the publisher is to maximize the amount of information about the public features in a revealed data set, while keeping the information leaked about the private features bounded. The goal of this paper is to analyze the performance of privacy mechanisms that are constructed to match the distribution learned from the data set. Two distinct scenarios are considered: (i) mechanisms are designed to provide a privacy guarantee for the learned distribution; and (ii) mechanisms are designed to provide a privacy guarantee for every distribution in a given neighborhood of the learned distribution. For the first scenario, given any privacy mechanism, upper bounds on the difference between the privacy-utility guarantees for the learned and true distributions are presented. In the second scenario, upper bounds on the reduction in utility incurred by providing a uniform privacy guarantee are developed.
On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs
Venue: arXiv cs.SI 2018
Year: 2018

Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted graph, the goal is to detect overlapping communities incrementally as the graph is dynamically changing. We propose an efficient algorithm, called \textit{randomized Speaker-Listener Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of label propagation. Besides detecting high-quality communities, rSLPA can incrementally update the detected communities after a batch of edge insertion and deletion operations. To the best of our knowledge, rSLPA is the first algorithm that can incrementally capture the same communities as those obtained by applying the detection algorithm from the scratch on the updated graph. Extensive experiments are conducted on both synthetic and real-world datasets, and the results show that our algorithm can achieve high accuracy and efficiency at the same time.
Challenges of the Dynamic Detection of Functionally Similar Code Fragments
Venue: arXiv cs.SE 2018
Year: 2018

Classic clone detection approaches are hardly capable of finding redundant code that has been developed independently, i.e., is not the result of copy&paste. To automatically detect such functionally similar code of independent origin, we experimented with a dynamic detection approach that applies random testing to selected chunks of code similar to Jiang&Su's approach. We found that such an approach faces several limitations in its application to diverse Java systems. This paper details on our insights regarding these challenges of dynamic detection of functionally similar code fragments. Our findings support a substantiated discussion on detection approaches and serve as a starting point for future research.
Private Information Retrieval Through Wiretap Channel II: Privacy Meets Security
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of private information retrieval through wiretap channel II (PIR-WTC-II). In PIR-WTC-II, a user wants to retrieve a single message (file) privately out of $M$ messages, which are stored in $N$ replicated and non-communicating databases. An external eavesdropper observes a fraction $μ_n$ (of its choice) of the traffic exchanged between the $n$th database and the user. In addition to the privacy constraint, the databases should encode the returned answer strings such that the eavesdropper learns absolutely nothing about the \emph{contents} of the databases. We aim at characterizing the capacity of the PIR-WTC-II under the combined privacy and security constraints. We obtain a general upper bound for the problem in the form of a max-min optimization problem, which extends the converse proof of the PIR problem under asymmetric traffic constraints. We propose an achievability scheme that satisfies the security constraint by encoding a secret key, which is generated securely at each database, into an artificial noise vector using an MDS code. The user and the databases operate at one of the corner points of the achievable scheme for the PIR under asymmetric traffic constraints such that the retrieval rate is maximized under the imposed security constraint. The upper bound and the lower bound match for the case of $M=2$ and $M=3$ messages, for any $N$, and any $\boldsymbolμ=(μ_1, \cdots, μ_N)$.
Angle-Monotone Graphs: Construction and Local Routing
Venue: arXiv cs.CG 2018
Year: 2018

A geometric graph in the plane is angle-monotone of width $γ$ if every pair of vertices is connected by an angle-monotone path of width $γ$, a path such that the angles of any two edges in the path differ by at most $γ$. Angle-monotone graphs have good spanning properties. We prove that every point set in the plane admits an angle-monotone graph of width $90^\circ$, hence with spanning ratio $\sqrt 2$, and a subquadratic number of edges. This answers an open question posed by Dehkordi, Frati and Gudmundsson. We show how to construct, for any point set of size $n$ and any angle $α$, $0 < α< 45^\circ$, an angle-monotone graph of width $(90^\circ+α)$ with $O(\frac{n}α)$ edges. Furthermore, we give a local routing algorithm to find angle-monotone paths of width $(90^\circ+α)$ in these graphs. The routing ratio, which is the ratio of path length to Euclidean distance, is at most $1/\cos(45^\circ + \fracα{2})$, i.e., ranging from $\sqrt 2 \approx 1.414$ to $2.613$. For the special case $α= 30^\circ$, we obtain the $Θ_6$-graph and our routing algorithm achieves the known routing ratio 2 while finding angle-monotone paths of width $120^\circ$.